Add onnx files

LICENSE

@@ -0,0 +1,202 @@
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README.md

@@ -1,155 +1,53 @@
 ---
-base_model:
-- microsoft/deberta-v3-small
-datasets:
-- anthonyyazdaniml/gliner-biomed-pre-training
-- anthonyyazdaniml/gliner-biomed-post-training
-language:
-- en
-library_name: gliner
+library_name: glitext
 license: apache-2.0
-metrics:
-- f1
-pipeline_tag: token-classification
 tags:
-- NER
-- GLiNER
-- information extraction
-- encoder
-- entity recognition
-- biomed
+- glitext
+glitext:
+  name: small-biomed
+  label: GliText Biomedical Recognition (Fast)
+  description: An efficient zero-shot named entity recognition model tuned for biomedical text extraction with high throughput (speed).
+  recognition: true
+  classification: false
+  association: false
+  span_mode: true
+  size_gb: 0.62
+  hf_repo: rpeel/glitext-small-biomed
+  source_url: Ihor/gliner-biomed-small-v1.0
 ---
 
-# GLiNER-BioMed
+# rpeel/glitext-small-biomed
 
-The model was presented in the paper [GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition](https://arxiv.org/abs/2504.00676).
+An efficient zero-shot named entity recognition model tuned for biomedical text extraction with high throughput (speed).
 
-The code is available at [https://github.com/ds4dh/GLiNER-biomed](https://github.com/ds4dh/GLiNER-biomed).
+## Requirements
 
-**GLiNER** is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoders (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.
+To download this model to the SAS GLiText server:
 
-
-**GLiNER-biomed**, developed in collaboration with [DS4DH](https://www.unige.ch/medecine/radio/en/research-groups/1035teodoro) from the University of Geneva, introduces a specialized suite of efficient open biomedical NER models based on the GLiNER framework. GLiNER-biomed leverages synthetic annotations distilled from large generative biomedical language models to achieve state-of-the-art zero-shot and few-shot performance in biomedical entity recognition tasks.
-
-### How to Use
-Install the official GLiNER library with pip:
-```bash
-pip install gliner -U
 ```
-
-After installing the GLiNER library, you can easily load a GLiNER-biomed model and perform named entity recognition:
-
-```python
-from gliner import GLiNER
-
-model = GLiNER.from_pretrained("Ihor/gliner-biomed-small-v1.0")
-
-text = """
-The patient, a 45-year-old male, was diagnosed with type 2 diabetes mellitus and hypertension.
-He was prescribed Metformin 500mg twice daily and Lisinopril 10mg once daily. 
-A recent lab test showed elevated HbA1c levels at 8.2%.
-"""
-
-labels = ["Disease", "Drug", "Drug dosage", "Drug frequency", "Lab test", "Lab test value", "Demographic information"]
-
-entities = model.predict_entities(text, labels, threshold=0.5)
-
-for entity in entities:
-    print(entity["text"], "=>", entity["label"])
+POST /v1/models/download?name=small-biomed
 ```
 
-Expected output:
+To download and load into memory in one step:
+
 ```
-45-year-old male => Demographic information
-type 2 diabetes mellitus => Disease
-hypertension => Disease
-Metformin => Drug
-500mg => Drug dosage
-twice daily => Drug frequency
-Lisinopril => Drug
-10mg => Drug dosage
-once daily => Drug frequency
-HbA1c levels => Lab test
-8.2% => Lab test value
+PUT /v1/models?name=small-biomed
 ```
 
-### Benchmarking
-
-We examined our models on 8 complex real-world datasets and compared them with other GLiNER models.
-
-| Model               	| F1-score | Macro mean F1 | Macro median F1 | Weighted F1 |
-|------------------------|----------|----------------|------------------|-------------|
-| **Large models**   	|      	|            	|              	|         	|
-| [NuNER Zero](https://huggingface.co/numind/NuNER_Zero)         	| 40.87	| 21.79      	| 13.94        	| 33.67   	|
-| [NuNER Zero span](https://huggingface.co/numind/NuNER_Zero-span)    	| 40.26	| 22.51      	| 14.27        	| 32.52   	|
-| [GLiNER bio v0.1](https://huggingface.co/urchade/gliner_large_bio-v0.1)    	| 42.34	| 27.10      	| 24.44        	| 38.32   	|
-| [GLiNER bio v0.2](https://huggingface.co/urchade/gliner_large_bio-v0.2)    	| 38.66	| 25.36      	| 17.02        	| 32.42   	|
-| [GLiNER v1.0](https://huggingface.co/urchade/gliner_large-v1)        	| 47.77	| 29.60      	| 21.13        	| 40.78   	|
-| [GLiNER v2.0](https://huggingface.co/urchade/gliner_large-v2)        	| 37.38	| 21.42      	| 15.44        	| 33.11   	|
-| [GLiNER v2.1](https://huggingface.co/urchade/gliner_large-v2.1)        	| 48.04	| 29.75      	| 28.20        	| 43.43   	|
-| [GLiNER news v2.1](https://huggingface.co/EmergentMethods/gliner_large_news-v2.1)   	| 48.99	| 31.79      	| 33.77        	| 45.13   	|
-| [GLiNER v2.5](https://huggingface.co/gliner-community/gliner_large-v2.5)        	| 53.81	| 35.22      	| 35.65        	| 51.57   	|
-| **[GLiNER-biomed](https://huggingface.co/Ihor/gliner-biomed-large-v1.0)**  	| **59.77**| **40.67**  	| **42.65**    	| **58.40**   |
-| **[GLiNER-biomed-bi](https://huggingface.co/Ihor/gliner-biomed-bi-large-v1.0)**   	| 54.90	| 35.78      	| 31.66        	| 50.46   	|
-| **Base models**    	|      	|            	|              	|         	|
-| [GLiNER v1.0](https://huggingface.co/urchade/gliner_medium-v1)        	| 41.61	| 24.98      	| 10.27        	| 31.59   	|
-| [GLiNER v2.0](https://huggingface.co/urchade/gliner_medium-v2)        	| 34.33	| 24.48      	| 22.01        	| 30.58   	|
-| [GLiNER v2.1](https://huggingface.co/urchade/gliner_medium-v2.1)        	| 40.25	| 25.26      	| 14.41        	| 32.64   	|
-| [GLiNER news v2.1](https://huggingface.co/EmergentMethods/gliner_medium_news-v2.1)   	| 41.59	| 27.16      	| 17.74        	| 34.44   	|
-| [GLiNER v2.5](https://huggingface.co/gliner-community/gliner_base-v2.5)        	| 46.49	| 30.93      	| 25.26        	| 44.68   	|
-| **[GLiNER-biomed](https://huggingface.co/Ihor/gliner-biomed-base-v1.0)**  	| 54.37| **36.20**  	| **41.61**    	| 53.05   |
-| **[GLiNER-biomed-bi](https://huggingface.co/Ihor/gliner-biomed-bi-base-v1.0)**   	| **58.31**	| 35.22      	| 32.39        	| **54.91**   	|
-| **Small models**   	|      	|            	|              	|         	|
-| [GLiNER v1.0](https://huggingface.co/urchade/gliner_small-v1)        	| 40.99	| 22.81      	| 7.86         	| 31.15   	|
-| [GLiNER v2.0](https://huggingface.co/urchade/gliner_small-v2)        	| 33.55	| 21.12      	| 15.76        	| 28.78   	|
-| [GLiNER v2.1](https://huggingface.co/urchade/gliner_small-v2.1)        	| 38.45	| 23.25      	| 10.92        	| 30.67   	|
-| [GLiNER news v2.1](https://huggingface.co/EmergentMethods/gliner_small_news-v2.1)   	| 39.15	| 24.96      	| 14.48        	| 33.10   	|
-| [GLiNER v2.5](https://huggingface.co/gliner-community/gliner_small-v2.5)        	| 38.21	| 28.53      	| 18.01        	| 36.88   	|
-| **[GLiNER-biomed](https://huggingface.co/Ihor/gliner-biomed-small-v1.0)**  	| 52.53| **34.49**  	| **38.17**    	| 50.87   |
-| **[GLiNER-biomed-bi](https://huggingface.co/Ihor/gliner-biomed-bi-small-v1.0)**   	| **56.93**	| 33.88      	| 33.61        	| **53.12**   	|
+## Source Model
 
+Exported from [Ihor/gliner-biomed-small-v1.0](https://huggingface.co/Ihor/gliner-biomed-small-v1.0).
+See the [original model card](https://huggingface.co/Ihor/gliner-biomed-small-v1.0) for full architecture and training details.
 
+## Security Scan
 
-### Join Our Discord
+Scanned with [modelaudit](https://github.com/promptfoo/modelaudit) v0.2.40 on 2026-04-27. 29/29 checks passed. [Full results](modelaudit.json).
 
-Connect with our community on Discord for news, support, and discussion about our models. Join [https://discord.gg/dkyeAgs9DG](https://discord.gg/dkyeAgs9DG).
 
-## Citation
+| File | Size | SHA-256 |
+|------|------|---------|
+| `model.onnx` | 611.3 MB | `3802e22c6358e1c9…` |
 
-### This work
-If you use GLiNER-biomed models in your work, please cite:
-
-```bibtex
-@misc{yazdani2025glinerbiomedsuiteefficientmodels,
-      title={GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition}, 
-      author={Anthony Yazdani and Ihor Stepanov and Douglas Teodoro},
-      year={2025},
-      eprint={2504.00676},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2504.00676}, 
-}
-```
-
-### Previous work
-```bibtex
-@misc{zaratiana2023gliner,
-      title={GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer},
-      author={Urchade Zaratiana and Nadi Tomeh and Pierre Holat and Thierry Charnois},
-      year={2023},
-      eprint={2311.08526},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-```
+## License
 
-```bibtex
-@misc{stepanov2024gliner,
-      title={GLiNER multi-task: Generalist Lightweight Model for Various Information Extraction Tasks},
-      author={Ihor Stepanov and Mykhailo Shtopko},
-      year={2024},
-      eprint={2406.12925},
-      archivePrefix={arXiv},
-      primaryClass={id='cs.LG' full_name='Machine Learning' is_active=True alt_name=None in_archive='cs' is_general=False description='Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.'}
-}
-```
+[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0). Derived from [Ihor/gliner-biomed-small-v1.0](https://huggingface.co/Ihor/gliner-biomed-small-v1.0) by [Ihor](https://huggingface.co/Ihor).

gliner_config.json

@@ -110,7 +110,7 @@
   "lr_encoder": "1e-5",
   "lr_others": "5e-5",
   "max_grad_norm": 1.0,
-  "max_len": 2048,
+  "max_len": 384,
   "max_neg_type_ratio": 1,
   "max_types": 30,
   "max_width": 12,
@@ -134,7 +134,7 @@
   "subtoken_pooling": "first",
   "train_batch_size": 8,
   "train_data": "data/ner_batches_8b_tuned_100k.json",
-  "transformers_version": "4.48.0.dev0",
+  "transformers_version": "4.48.3",
   "val_data_dir": "none",
   "vocab_size": 128003,
   "warmup_ratio": 0.1,

model.onnx

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+oid sha256:3802e22c6358e1c9bce2dea3dca6ebd883c10d172e1230233fd6756ee489128e
+size 611279071

modelaudit.json

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+  "tool_version": "0.2.40",
+  "scanned_at": "2026-04-27T03:09:39Z",
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+      "sha256": "3802e22c6358e1c9bce2dea3dca6ebd883c10d172e1230233fd6756ee489128e"
+    }
+  },
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+        "severity": "info",
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+        "details": {
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+        "severity": "info",
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+          "total_outputs": 2048,
+          "analysis_method": "structural_analysis"
+        },
+        "why": "Weight values that are orders of magnitude larger than typical can cause numerical instability, overflow attacks, or may encode hidden data. Detection uses statistical analysis rather than name-based classification to avoid security bypasses.",
+        "timestamp": 1777259375.8744645,
+        "type": "onnx_check",
+        "rule_code": "S802"
+      },
+      {
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+    "checks": [
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